论文标题
通过学习预测跨维运动的自我监督的人类活动识别
Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion
论文作者
论文摘要
我们建议使用智能手机加速度计数据使用自我监督学习来识别人类活动。我们提出的解决方案包括两个步骤。首先，通过训练深层卷积神经网络来预测加速度计值的一部分，学习了未标记的输入信号的表示。我们的模型利用了一种新的方案，以利用X和Y维度以及Z轴的过去值以预测Z维度中的值。这种跨二维预测方法会导致有效的借口训练，我们的模型学会了提取强烈的表示。接下来，我们将卷积块冻结，并将权重转移到针对人类活动识别的下游网络中。对于此任务，我们在冷冻网络的末端添加了许多完全连接的图层，并使用标记的加速度计信号训练添加的图层，以学习对人类活动进行分类。我们在三个公开可用的人类活动数据集上评估了方法的性能：UCI HAR，MOTIONSESSENS和HAPT。结果表明，我们的方法表现优于现有方法，并设置新的最新结果。
We propose the use of self-supervised learning for human activity recognition with smartphone accelerometer data. Our proposed solution consists of two steps. First, the representations of unlabeled input signals are learned by training a deep convolutional neural network to predict a segment of accelerometer values. Our model exploits a novel scheme to leverage past and present motion in x and y dimensions, as well as past values of the z axis to predict values in the z dimension. This cross-dimensional prediction approach results in effective pretext training with which our model learns to extract strong representations. Next, we freeze the convolution blocks and transfer the weights to our downstream network aimed at human activity recognition. For this task, we add a number of fully connected layers to the end of the frozen network and train the added layers with labeled accelerometer signals to learn to classify human activities. We evaluate the performance of our method on three publicly available human activity datasets: UCI HAR, MotionSense, and HAPT. The results show that our approach outperforms the existing methods and sets new state-of-the-art results.
